文献类型：期刊文献

英文题名：Forecasting sea surface temperature during typhoon events in the Bohai Sea using spatiotemporal neural networks

作者：He, Hailun[1,2];Shi, Benyun[3,4];Hao, Yingjian[3,4];Feng, Liu[3,4];Lyu, Xinyan[5];Ling, Zheng[6]

机构：[1]Minist Nat Resources, Inst Oceanog 2, State Key Lab Satellite Ocean Environm Dynam, Hangzhou 310012, Peoples R China;[2]Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai 519082, Peoples R China;[3]Nanjing Tech Univ, Coll Comp & Informat Engn, Nanjing 211816, Peoples R China;[4]Nanjing Tech Univ, Coll Artificial Intelligence, Nanjing 211816, Peoples R China;[5]China Meteorol Adm, Natl Meteorol Ctr, Beijing 100081, Peoples R China;[6]Guangdong Ocean Univ, Coll Ocean & Meteorol, Dept Educ Guangdong Prov, Key Lab Climate Resources & Environm Continental S, Zhanjiang 524088, Peoples R China

年份：2024

卷号：309

外文期刊名：ATMOSPHERIC RESEARCH

收录：SCI-EXPANDED(收录号：WOS:001286558400001)、、EI(收录号：20243116796652)、Scopus(收录号：2-s2.0-85199942025)、WOS

基金：This study is supported in part by the National Natural Science Foundation of China (Grant no. 42227901) , in part by the National Natural Science Foundation of China (NSFC) and the Research Grants Council (RGC) of Hong Kong Joint Research Scheme (Grant no. 62261160387) , in part by the Global Change and Air-Sea Interaction II Program (Grant no. GASI-01-WPAC-STspr) , and in part by the Post-graduate Research & Practice Innovation Program of Jiangsu Province (Grant no. SJCX23_0435) . The authors declare that they have no conflict of interest.

语种：英文

外文关键词：Sea surface temperature; Typhoon events; Neural networks; Attention mechanism; Convolutional long-short term memory; Predictive recurrent neural network

外文摘要：Advanced neural network methods can effectively combine temporal and spatial information, allowing us to exploit complex relationships in the prediction of sea surface temperature (SST). Therefore, the authors have developed an attention-based context fusion network model recently, known as ACFN, to enhance the underlying spatiotemporal correlations in SST data. In a previous preliminary long-term evaluation, ACFN has demonstrated substantial improvements over several state-of-the-art baseline models, such as Convolutional Long-Short Term Memory (ConvLSTM) and Predictive Recurrent Neural Network (PredRNN). However, these methods have not been widely used for predicting SST during typhoons, and their performance has not been thoroughly evaluated. This study focuses on three specific typhoons, In-fa (2021), Lekima (2019), and Rumbia (2018), as case studies. The results show that ACFN consistently outperforms ConvLSTM and PredRNN, as demonstrated during Typhoon In-fa with a mean absolute error of 0.27 degrees C and a root-mean-square error of 0.33 degrees C for a 1-day forecast time. This study also examines the impact of forecast time on predictive skill, revealing that the performance of ACFN, as expected, gradually decreases with longer forecast times. This thorough evaluation of the ACFN model provides a valuable reference for using deep learning in predicting regional typhoon SST. Plain language summary: In previous work, we developed an attention-based context fusion network model. This spatiotemporal neural network model has been successfully applied to short-term sea surface temperature (SST) prediction in the Bohai Sea, utilizing only current and previous 1- to 10-day SST data as input to forecast SST for the subsequent 1-to 10-day period. In this study, we further evaluated the capability of the model to predict SST during typhoons in the Bohai Sea, and the results were very promising. When predicting SST during Typhoon Infa, the present spatiotemporal neural network model had a mean absolute error of only 0.27 degrees C for a 1-day forecast time, and a correlation coefficient of 0.74. On the other hand, comparison with baseline spatiotemporal neural network models shows that the present model is more effective in extracting input information. The current model has resolved the constraint that its predictive capability does not diminish as the forecast time increases, thereby enhancing the rationality of the prediction outcomes. In conclusion, the present spatiotemporal neural network model demonstrates its efficacy in forecasting typhoon-induced SST in the Bohai Sea, offering valuable insights for applications in meteorology and oceanography.